System identification: theory for the user
System identification: theory for the user
Essentials of fuzzy modeling and control
Essentials of fuzzy modeling and control
Evolving fuzzy rule based controllers using genetic algorithms
Fuzzy Sets and Systems
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
A GA paradigm for learning fuzzy rules
Fuzzy Sets and Systems - Special issue on connectionist and hybrid connectionist systems for approximate reasoning
Evolving rule-based models: a tool for design of flexible adaptive systems
Evolving rule-based models: a tool for design of flexible adaptive systems
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Extracting fuzzy control rules from experimental human operatordata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Semantic constraints for membership function optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A self-learning fuzzy logic controller using genetic algorithms with reinforcements
IEEE Transactions on Fuzzy Systems
Assessment of linguistic dynamic cause-and-effect rules with delays
ACC'09 Proceedings of the 2009 conference on American Control Conference
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An approach to fuzzy rule-based model (FRB) synthesis from data based on evolutionary algorithm using indices of the rules is presented in the paper. The resulting models are transparent and existing knowledge could easily be incorporated at the initialisation stage. The main difference between the proposed approach and the previous ones is the treatment of a small part of the complete rule set only, which allows an interpretable resulting model to be achieved and the dimension of chromosome considered in the evolutionary algorithm to be significantly reduced. A specific encoding mechanism is presented considering only the rules, which actually participate in the model. They are represented by their indices and membership functions' parameters. It allows treatment of the problem of structure and parameter identification with practically meaningful dimensions (tens of fuzzy linguistic terms and fuzzy linguistic variables), while most of the other approaches indirectly suppose a small number of inputs and linguistic terms. As a result, the synthesised fuzzy model is significantly more transparent then other black-box types of models like neural networks, polynomial models and also FRB models considering the complete rule set, since a partial set of fuzzy rules (normally some tens) is easy to be inspected and explained. At the same time, this model is significantly more flexible than first principle models. This approach is applied to modelling of components of heating ventilating and air-conditioning systems and validated with real experimental data. It has potential applications in simulation, control and fault detection and diagnosis.